CRO METHODS BASED ON MACHINE LEARNING

Abstract

This article analyzes conversion rate optimization (CRO) methodologies based on machine learning. Existing CRO systems, including their functionality and efficiency, are examined in detail. The article provides an overview of various methods already used in CRO systems, such as A/B testing, web analytics, and content personalization. Special attention is paid to the implementation of machine learning methods in the CRO field. The advantages of using ML are highlighted, such as the ability to analyze large volumes of data, speed of decision-making, and process automation. A specific plan for implementing ML in the CRO system is presented, including the use of algorithms for predicting changes in conversions, identifying user patterns, and optimizing content. The article highlights the importance of development and modernity in the CRO field, as well as points out the potential advantages of using machine learning to enhance the efficiency of conversion optimization. The purpose of the work is to improve existing CRO methods by using machine learning methods for more accurate prediction of user behavior and more effective content personalization. The research methodology is based on the analysis of big data, the use of neural networks, and cluster analysis. In particular, a cluster analysis method is applied for grouping text descriptions of goods and a recommendation system based on a collaborative filtering algorithm implemented by a deep neural network. The scientific novelty lies in the use of machine learning for an adaptive recommendation and search system based on neural networks and cluster analysis. The conclusion is the significant potential of using machine learning in the field of CRO, demonstrating that the implementation of deep neural networks and cluster analysis algorithms can be effective in predicting conversions and personalizing content.